We use the test dataset to evaluate the proposed DPHR method and compare it with state-of-the-art methods, including seven deep-learning-based methods developed for the conventional cameras [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, one traditional model based method developed for the polarization camera [8]}, and one deep-learning-based method developed also for the polarization camera [9]}. For all methods, we used the codes and pretrain weights provided by the authors. Fig. REF , REF  illustrates the qualitative results of the comparative study. We can observe that images reconstructed with the DPHR method can recover more accurate details in all properly exposed and poorly exposed regions, and alleviate color distortions and visible artifacts. The HDR-GAN [1]} and DeepHDR [2]} results are relatively bright, and has difficulty restoring details in the over-exposed areas. The ENet [3]} result tends to be overly bright and smooth, as it over-enhances the extracted illumination features. Additionally, it's unable to improve HDR estimation for properly exposed regions. The DrTMO [4]} method suffers from blocking artifacts, and has difficulty recovering details in all properly exposed and poorly exposed regions. The DRCP [5]} method can restore details in the poorly exposed regions, but exhibits color distortions. Furthermore, the result is generally darker, thus contents lost in the under-exposed regions can not be restored. The PHDR [8]} result is overall bright where pixels remain lost in the poorly exposed regions. The DPSHDR [9]} result reveals more details, but remains unable to reconstruct sufficient details in both under- and over-exposed regions.
